Bibliographic record
Abstract
Purpose This paper aims to test the extent to which downward bias due to a floating-point exception in probability of informed trading (PIN) estimates obtained using the Easley, Hvidkjaer and O’Hara (EHO; 2002) method is remedied using the Yan and Zhang (YZ; 2012) method. The paper also aims to test the sample-size sensitivity of EHO PIN and identify PIN determinants for acquirers and targets in the biotech sector. Design/methodology/approach EHO and YZ PIN performances are compared for US biotech acquirers and targets around their mergers and acquisition (M&A) announcements. The sampling method of Kryzanowski and Lazrak (2007) is used to assess sample-size sensitivity of announcement window EHO PIN estimates. Cross-sectional regressions are estimated to identify PIN determinants. Findings EHO and YZ PIN are not significantly different. EHO PIN exhibits significant sample-size sensitivity. Information leakage prior to M&A announcements is strongly affected by some firm characteristics. Significant determinants of PIN behavior around M&A announcements include insider and institutional holdings and research and development (R&D) expense. Research limitations/implications Findings imply that PIN partially reflects the activities of insiders and other informed investors about takeover intentions. Subsequent research can examine PIN behavior around pre-announcement rumors for M&As in the same or other industries and for potential targets that are peers of the M&A targets. Originality/value This paper contributes to the ongoing debate in the empirical finance literature on whether PIN measures informed trading by examining its behavior and the importance of some methodological issues associated with its use in examining market behavior around M&A announcements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".